From d91a039e0eda6fd70dcd22672b8ce1817c1ca50e Mon Sep 17 00:00:00 2001
From: Chunseok Lee <chunseok.lee@samsung.com>
Date: Thu, 5 Mar 2020 15:10:09 +0900
Subject: catch up to tizen_5.5 and remove unness dir

- update to tizen_5.5
- remove dirs
---
 runtimes/libs/cker/include/cker/operation/Add.h | 95 +++++++++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 runtimes/libs/cker/include/cker/operation/Add.h

(limited to 'runtimes/libs/cker/include/cker/operation/Add.h')

diff --git a/runtimes/libs/cker/include/cker/operation/Add.h b/runtimes/libs/cker/include/cker/operation/Add.h
new file mode 100644
index 000000000..703d617f8
--- /dev/null
+++ b/runtimes/libs/cker/include/cker/operation/Add.h
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __NNFW_CKER_ADD_H__
+#define __NNFW_CKER_ADD_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+struct AddParam
+{
+  // Shape dependent / common to data / op types.
+  // BroadcastableOpCategory broadcast_category;
+  // uint8 inference params.
+  int32_t input1_offset;
+  int32_t input2_offset;
+  int32_t output_offset;
+  int32_t output_multiplier;
+  int32_t output_shift;
+  // Add / Sub, not Mul, uint8 inference params.
+  int32_t left_shift;
+  int32_t input1_multiplier;
+  int32_t input1_shift;
+  int32_t input2_multiplier;
+  int32_t input2_shift;
+  // uint8, etc, activation params.
+  int32_t quantized_activation_min;
+  int32_t quantized_activation_max;
+  // float activation params.
+  float float_activation_min;
+  float float_activation_max;
+
+  // Processed output dimensions.
+  // Let input "a" be the one that broadcasts in the faster-changing dimension.
+  // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and
+  // {b0, b1, b2, b3, b4},
+  // broadcast_shape[4] = b0 = a0.
+  // broadcast_shape[3] = b1; a1 = 1.
+  // broadcast_shape[2] = b2 = a2.
+  // broadcast_shape[1] = a3; b3 = 1.
+  // broadcast_shape[0] = b4 = a4.
+  // int broadcast_shape[5];
+};
+
+template <typename T>
+inline void Add(const AddParam &params, const Shape &input1_shape, const T *input1_data,
+                const Shape &input2_shape, const T *input2_data, const Shape &output_shape,
+                T *output_data)
+{
+  const int32_t flat_size = MatchingFlatSize(input1_shape, input2_shape, output_shape);
+  for (int i = 0; i < flat_size; ++i)
+  {
+    output_data[i] = ActivationFunctionWithMinMax(input1_data[i] + input2_data[i],
+                                                  params.quantized_activation_min,
+                                                  params.quantized_activation_max);
+  }
+}
+
+inline void Add(const AddParam &params, const Shape &input1_shape, const float *input1_data,
+                const Shape &input2_shape, const float *input2_data, const Shape &output_shape,
+                float *output_data)
+{
+  const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape);
+  for (int i = 0; i < size; i++)
+  {
+    auto x = input1_data[i] + input2_data[i];
+    output_data[i] =
+        ActivationFunctionWithMinMax(x, params.float_activation_min, params.float_activation_max);
+  }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_ADD_H__
-- 
cgit v1.2.3